A novel prognostic model based on urea cycle-related gene signature for colorectal cancer

Guo, Haiyang; Wang, Yuanbiao; Gou, Lei; Wang, Xiaobo; Tang, Yong; Wang, Xianfei

doi:10.3389/fsurg.2022.1027655

Cited by 5 publications

(3 citation statements)

References 61 publications

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“…In contrast, serum CysC has been monitored in several clinical studies, showing a strong association with GFR estimation [ 66 , 67 ]. In fact, higher levels of serum CysC and a decline of the GFR function are closely linked, hence representing a reference standard for kidney injury in critically ill AKI patients [ 64 , 68 ]. In addition, increased concentration of serum CysC has been also associated with increased HbA1c levels in diabetic patients who did not show any signs of kidney damage [ 69 ].…”

Section: Resultsmentioning

confidence: 99%

Recent Advances of Proteomics in Management of Acute Kidney Injury

Pejchinovski¹,

Türkkan²,

Pejchinovski

2023

Diagnostics

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Acute Kidney Injury (AKI) is currently recognized as a life-threatening disease, leading to an exponential increase in morbidity and mortality worldwide. At present, AKI is characterized by a significant increase in serum creatinine (SCr) levels, typically followed by a sudden drop in glomerulus filtration rate (GFR). Changes in urine output are usually associated with the renal inability to excrete urea and other nitrogenous waste products, causing extracellular volume and electrolyte imbalances. Several molecular mechanisms were proposed to be affiliated with AKI development and progression, ultimately involving renal epithelium tubular cell-cycle arrest, inflammation, mitochondrial dysfunction, the inability to recover and regenerate proximal tubules, and impaired endothelial function. Diagnosis and prognosis using state-of-the-art clinical markers are often late and provide poor outcomes at disease onset. Inappropriate clinical assessment is a strong disease contributor, actively driving progression towards end stage renal disease (ESRD). Proteins, as the main functional and structural unit of the cell, provide the opportunity to monitor the disease on a molecular level. Changes in the proteomic profiles are pivotal for the expression of molecular pathways and disease pathogenesis. Introduction of highly-sensitive and innovative technology enabled the discovery of novel biomarkers for improved risk stratification, better and more cost-effective medical care for the ill patients and advanced personalized medicine. In line with those strategies, this review provides and discusses the latest findings of proteomic-based biomarkers and their prospective clinical application for AKI management.

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Section: Resultsmentioning

confidence: 99%

Recent Advances of Proteomics in Management of Acute Kidney Injury

Pejchinovski¹,

Türkkan²,

Pejchinovski

2023

Diagnostics

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“…In this research, network pharmacology and combined bioinformatics were used to analyze the targets of traditional Chinese medicine extracts quercetin and kaempferol, construct and screen the interaction network between drugs and CRC pathogenic genes, and finally screen three prognostic related genes: CDKN2A, SERPINE1, and MMP3 [74][75][76][77][78][79].…”

Section: Discussionmentioning

confidence: 99%

Network pharmacology and bioinformatics were used to construct a prognostic model and immunoassay of core target genes in the combination of quercetin and kaempferol in the treatment of colorectal cancer

Gu¹,

Tang²,

Hu³

et al. 2023

J. Cancer

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Purpose: CRC is a malignant tumor seriously threatening human health. Quercetin and kaempferol are representative components of traditional Chinese medicine (TCM). Previous studies have shown that both quercetin and kaempferol have antitumor pharmacological effects, nevertheless, the underlying mechanism of action remains unclear. To explore the synergy and mechanism of quercetin and kaempferol in colorectal cancer. Methods: In this study, network pharmacology, and bioinformatics are used to obtain the intersection of drug targets and disease genes. Training gene sets were acquired from the TCGA database, acquired prognostic-related genes by univariate Cox, multivariate Cox, and Lasso-Cox regression models, and validated in the GEO dataset. We also made predictions of the immune function of the samples and used molecular docking to map a model for binding two components to prognostic genes. Results: Through Lasso-Cox regression analysis, we obtained three models of drug target genes. This model predicts the combined role of quercetin and kaempferol in the treatment and prognosis of CRC. Prognostic genes are correlated with immune checkpoints and immune infiltration and play an adjuvant role in the immunotherapy of CRC. Conclusion: Core genes are regulated by quercetin and kaempferol to improve the patient's immune system and thus assist in the treatment of CRC.

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“…We used expression levels and coefficients of feature genes to construct a final Riskscore prognostic model. The calculation formula of the specific Riskscore prognostic model was as follows: Riskscore = ∑1ncoef)(genei*geneexpression)(genei, where “coef” represents the coefficient of the signature gene obtained by multivariate regression analysis, and “expression” represents the expression level of the signature gene [17].…”

Section: Methodsmentioning

confidence: 99%

Hepatocellular Carcinoma Subtyping and Prognostic Model Construction Based on Chemokine-Related Genes

Guo,

Huang,

Zhan

2023

Med Princ Pract

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Background: Chemokines not only regulate immune cells but also play significant roles in development and treatment of tumors and patient prognoses. However, these effects have not been fully explained in hepatocellular carcinoma (HCC). Materials and Methods: We conducted a clustering analysis of chemokine-related genes. We then examined the differences in survival rates and analyzed immune levels using single sample gene set enrichment analysis (ssGSEA) for each subtype. Based on chemokine-related genes of different subtypes, we built a prognostic model in The Cancer Genome Atlas (TCGA) dataset using the survival package and glmnet package and validated it in the Gene Expression Omnibus (GEO) dataset. We used univariate and multivariate regression analyses to select independent prognostic factors and used R package rms to draw a nomogram reflecting patient survival rates at 1, 3, and 5 years. Results: We identified two chemokine subtypes, and after screening, found that Cluster1 had higher survival rates than Cluster2. In addition, in terms of immune evaluation, stromal evaluation, ESTIMATE evaluation, immune abundance, immune function, and expressions of various immune checkpoints, immune levels of Cluster1 were significantly better than those of Cluster2. The immunophenoscore (IPS) of HCC patients in Cluster1 was significantly higher than that in Cluster2. Furthermore, we established a prognostic model consisting of 9 genes, which correlated with chemokines. Through testing, RiskScore was revealed as an independent prognostic factor, and the model could effectively predict HCC patients’ prognoses in both TCGA and GEO datasets. Conclusion: This study resulted in the development of a novel prognostic model related to chemokine genes, providing new targets and theoretical support for HCC patients.

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A novel prognostic model based on urea cycle-related gene signature for colorectal cancer

Cited by 5 publications

References 61 publications

Recent Advances of Proteomics in Management of Acute Kidney Injury

Recent Advances of Proteomics in Management of Acute Kidney Injury

Network pharmacology and bioinformatics were used to construct a prognostic model and immunoassay of core target genes in the combination of quercetin and kaempferol in the treatment of colorectal cancer

Hepatocellular Carcinoma Subtyping and Prognostic Model Construction Based on Chemokine-Related Genes

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